Model Comparison
DeepSeek-V4-Pro-Max vs MiMo-V2.5Which is better in 2026?
Both models are evenly matched across the benchmarks. MiMo-V2.5 is 9.5x cheaper per token.
Verdict: DeepSeek-V4-Pro-Max vs MiMo-V2.5 — which is better?
DeepSeek-V4-Pro-Max (by DeepSeek) and MiMo-V2.5 (by Xiaomi) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-V4-Pro-Max outperforms in 1 benchmarks (Terminal-Bench 2.0), while MiMo-V2.5 is better at 1 benchmark (SWE-Bench Pro). Both models are evenly matched across the benchmarks.
On price, MiMo-V2.5 is roughly 9.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Choose DeepSeek-V4-Pro-Max if…
- you want the most recent training data — it shipped Apr 2026
Choose MiMo-V2.5 if…
- cost matters — it's about 9.5x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-Max outperforms in 1 benchmarks (Terminal-Bench 2.0), while MiMo-V2.5 is better at 1 benchmark (SWE-Bench Pro).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-Max ($1.60/1M tokens) is 9.5x more expensive than MiMo-V2.5 ($0.17/1M tokens).
For output processing, DeepSeek-V4-Pro-Max ($3.20/1M tokens) is 9.5x more expensive than MiMo-V2.5 ($0.34/1M tokens).
In conclusion, DeepSeek-V4-Pro-Max is more expensive than MiMo-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-Max has 1289.2B more parameters than MiMo-V2.5, making it 414.8% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. Both models can generate responses up to 131,072 tokens.
Input Capabilities
Supported data types and modalities
MiMo-V2.5 supports multimodal inputs, whereas DeepSeek-V4-Pro-Max does not.
MiMo-V2.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-Max
MiMo-V2.5
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Pro-Max was released on 2026-04-23, while MiMo-V2.5 was released on 2026-04-22.
DeepSeek-V4-Pro-Max is 0 month newer than MiMo-V2.5.
Apr 23, 2026
2 months ago
1d newerApr 22, 2026
2 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Pro-Max is available from Novita, DeepInfra, DeepSeek, Fireworks, Together. MiMo-V2.5 is available from Novita, DeepInfra.
DeepSeek-V4-Pro-Max
MiMo-V2.5
Outputs Comparison
Key Takeaways
DeepSeek-V4-Pro-Max
View detailsDeepSeek
MiMo-V2.5
View detailsXiaomi
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-Max and MiMo-V2.5 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V4-Pro-Max vs MiMo-V2.5.